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   "metadata": {},
   "source": [
    "## Import Libraries and Functions and Define the Model"
   ]
  },
  {
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   "execution_count": 3,
   "id": "a978bf6c-0334-4ae9-8226-21a7b61bef9d",
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   "source": [
    "# Physics Informed Neural Network with Taylor Series\n",
    "\n",
    "from miscFun import *\n",
    "output_notebook()\n",
    "\n",
    "'''\n",
    "N_INPUT: The number of input bio-z dimensions for one heartbeat\n",
    "N_FEAT: The number of physiological features\n",
    "N_EXT: The number of features extracted by the CNN\n",
    "'''\n",
    "#model_DNN 函数定义了三个参数：心跳数据的输入维度数量 N_INPUT，生理特征的数量 N_FEAT，以及CNN提取的特征数量 N_EXT。\n",
    "def model_DNN(N_INPUT, N_FEAT=1, N_EXT=100):\n",
    "    # The input to the model is a 1D tensor representing a time series of heartbeat data, sampled with 250/8 points for 30 seconds \n",
    "    inp_beat=tf.keras.Input(shape=(N_INPUT))\n",
    "    \n",
    "    # Define the 1D CNN for NN feature extraction\n",
    "    # The input tensor is first expanded by one dimension (from 1D to 2D) to be compatible with the Conv1D layer\n",
    "    cnn1_1 = tf.keras.layers.Conv1D(32,5,activation='relu')(tf.keras.backend.expand_dims(inp_beat,axis=-1))\n",
    "    #tf.keras.layers.Conv1D(32, 5, activation='relu') 创建了一个一维卷积层，\n",
    "    #其中：32 表示卷积层中滤波器的数量。5 是每个滤波器的尺寸，即卷积窗口的大小。activation='relu' 指定了激活函数为ReLU\n",
    "    cnn1_2 = tf.keras.layers.Conv1D(64,3,activation='relu')(cnn1_1)\n",
    "    #创建了第二个一维卷积层，与第一个卷积层类似，但滤波器数量增加到64，卷积窗口大小变为3。这允许网络在更细粒度上学习特征。\n",
    "    mp_cnn1 = tf.keras.layers.MaxPooling1D(pool_size=3,strides=1)(cnn1_2)\n",
    "    #创建了一个一维最大池化层，指定了池化窗口的大小为3，池化窗口移动的步长为1，这意味着池化操作将应用于每个可能的位置，以减少特征的空间维度，同时保留尽可能多的信息。\n",
    "    fl_cnn1 = tf.keras.layers.Flatten()(mp_cnn1)\n",
    "    #将经过卷积和池化操作后的二维张量展平为一维张量。这是为了准备将特征输入到后续的全连接层中进行进一步的处理。\n",
    "    \n",
    "    # A fully connected layer further processes the flattened tensor and extracts N_EXT features\n",
    "    # 创建了一个全连接层，该层输出的节点数为N_EXT个，指定了使用ReLU激活函数，是将前一层的输出（fl_cnn）作为当前全连接层的输入\n",
    "    feat_ext = tf.keras.layers.Dense(N_EXT,activation='relu')(fl_cnn1) \n",
    "    \n",
    "    # Define physiological features (case study uses 3 features), each of these features is expected to be a 1D tensor\n",
    "    # 这行注释说明了接下来的代码将定义三个生理特征输入，案例研究中使用了三个特征。\n",
    "    inp_feat1 = tf.keras.Input(shape=(N_FEAT)) # feat 1 创建了一个输入层，用于接收第一个生理特征, shape=(N_FEAT) 指定了输入数据的形状\n",
    "    inp_feat2 = tf.keras.Input(shape=(N_FEAT)) # feat 2\n",
    "    inp_feat3 = tf.keras.Input(shape=(N_FEAT)) # feat 3\n",
    "    \n",
    "    # The extracted features and physiological features are concatenated together \n",
    "    #创建了一个拼接层，用于沿着指定的轴将多个输入张量拼接在一起。\n",
    "    #这里包含了三个生理特征的输入层 inp_feat1、inp_feat2 和 inp_feat3，以及之前通过全连接层 feat_ext 提取的特征。这些输入被作为一个列表传递给 Concatenate 层。\n",
    "    #feat_comb 包含了所有拼接在一起的特征，它将作为模型中下一个层的输入\n",
    "    feat_comb = tf.keras.layers.Concatenate()([inp_feat1,inp_feat2,inp_feat3,feat_ext])\n",
    "    \n",
    "    # A fully connected layer with is applied to the concatenated features\n",
    "    #全连接层 dense1_1，该层有60个节点，使用ReLU激活函数\n",
    "    dense1_1 = tf.keras.layers.Dense(60,activation='relu')(feat_comb) \n",
    "    #输出层 out，N_FEAT 是输出层的节点数，与函数 model_DNN 的参数相对应，表示模型最终预测的生理特征的数量，dense1_1的输出作为输入\n",
    "    out = tf.keras.layers.Dense(N_FEAT)(dense1_1) \n",
    "    \n",
    "    # Finally, the model is instantiated with the specified inputs and outputs\n",
    "    #创建了一个新的模型实例\n",
    "    model = tf.keras.Model(inputs=[inp_beat, inp_feat1, inp_feat2, inp_feat3], outputs=[out])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3147e93-a796-4077-b31c-248be2d20914",
   "metadata": {},
   "source": [
    "### Import a Demo Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "199f70e4-06d2-4848-9ae3-57deed7785bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   trial_id                                         bioz_beats  phys_feat_1  \\\n",
      "0         1  [4.674306050529548e-06, 3.873631220239295e-06,...     5.817552   \n",
      "1         1  [4.806376660377724e-06, 4.00727594328936e-06, ...     6.002678   \n",
      "2         1  [4.4183450820834e-06, 3.6333050096782107e-06, ...     5.632601   \n",
      "3         1  [4.076650431880851e-06, 3.4283849875982737e-06...     5.119284   \n",
      "4         1  [3.848732650901477e-06, 3.1940944086055694e-06...     4.833644   \n",
      "\n",
      "   phys_feat_2  phys_feat_3         sys  \n",
      "0   905.797101    97.832540  141.154438  \n",
      "1   905.923964    99.785117  143.502124  \n",
      "2   897.170462   100.450450  147.810844  \n",
      "3   919.250190   102.752581  150.845861  \n",
      "4   914.810008   102.519611  153.840739  \n"
     ]
    }
   ],
   "source": [
    "# load an example data for demo\n",
    "# pd.read_pickle：这是Pandas库提供的函数，用于读取pickle格式的文件\n",
    "# compression='gzip'：这个参数指定了文件压缩的类型。告诉 read_pickle 函数使用gzip压缩算法来解压缩pickle文件\n",
    "df_demo_data = pd.read_pickle('data_demo_pinn_bioz_bp',compression='gzip')\n",
    "print(df_demo_data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a40a95ce-5e05-4499-a4da-27394b896c3f",
   "metadata": {},
   "source": [
    "### Preprocess and Prepare the Train/Test Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bb49b2b3-be56-4654-b4ec-0134c8486bf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "标准化后的心跳数据前5行:\n",
      "0    [2.9279891461837044, 2.4172478429202755, 0.969...\n",
      "1    [3.012235475460972, 2.5024982809790393, 1.0585...\n",
      "2    [2.764714576204573, 2.2639465059145487, 0.8654...\n",
      "3    [2.5467514726318945, 2.1332303709962863, 0.887...\n",
      "4    [2.401365330729425, 1.9837790942355338, 0.7758...\n",
      "Name: bioz_beats_scaled, dtype: object\n",
      "\n",
      "标准化后的目标变量前5行:\n",
      "0   -0.952151\n",
      "1   -0.812206\n",
      "2   -0.555365\n",
      "3   -0.374448\n",
      "4   -0.195925\n",
      "Name: sys_scaled, dtype: float64\n",
      "\n",
      "标准化后的生理特征前5行:\n",
      "phys_feat_1_scaled: \n",
      "0      [1.048386639332269]\n",
      "1     [1.1888416577787004]\n",
      "2     [0.9080649136981447]\n",
      "3     [0.5186121459215011]\n",
      "4    [0.30189754244438355]\n",
      "Name: phys_feat_1_scaled, dtype: object\n",
      "\n",
      "phys_feat_2_scaled: \n",
      "0    [-0.09849125703922874]\n",
      "1    [-0.09499063973369752]\n",
      "2    [-0.33653323381721845]\n",
      "3     [0.27273092140839417]\n",
      "4     [0.15020931571389476]\n",
      "Name: phys_feat_2_scaled, dtype: object\n",
      "\n",
      "phys_feat_3_scaled: \n",
      "0     [1.145099680369798]\n",
      "1     [1.476112018152463]\n",
      "2     [1.588903218155048]\n",
      "3    [1.9791739417836047]\n",
      "4     [1.939679518748165]\n",
      "Name: phys_feat_3_scaled, dtype: object\n",
      "\n",
      "训练集的形状: (65, 34)\n",
      "测试集的形状: (813, 34)\n",
      "[[ 1.40342188  1.20696073  0.57106255 -0.35657866 -1.12391665 -1.37596205\n",
      "  -1.14565952 -0.71443923 -0.33976333 -0.14102986 -0.13168095 -0.25735724\n",
      "  -0.39948148 -0.42010217 -0.25465701  0.04960671  0.38448481  0.66621712\n",
      "   0.87761553  1.04565355  1.19983055  1.33863092  0.36487033  0.41580673\n",
      "  -0.05369714 -0.05369714 -0.05369714 -0.05369714 -0.05369714 -0.05369714\n",
      "  -0.05369714 -0.05369714 -0.05369714 -0.05369714]\n",
      " [ 2.90389556  2.43448161  1.07607542 -0.77574558 -2.33136327 -3.04604945\n",
      "  -2.90130425 -2.21531077 -1.38535853 -0.7508438  -0.50441766 -0.6002785\n",
      "  -0.78160097 -0.77792034 -0.48380902  0.03650999  0.66336824  1.32168704\n",
      "   1.98257189  2.56547973  0.87001177 -0.05369714 -0.05369714 -0.05369714\n",
      "  -0.05369714 -0.05369714 -0.05369714 -0.05369714 -0.05369714 -0.05369714\n",
      "  -0.05369714 -0.05369714 -0.05369714 -0.05369714]\n",
      " [ 1.69307606  1.51099574  0.8684041  -0.16417978 -1.15624948 -1.66559626\n",
      "  -1.63324588 -1.29902554 -0.88215377 -0.47599797 -0.15702056 -0.02590736\n",
      "  -0.0895676  -0.1918806  -0.13975898  0.12283368  0.50071164  0.86537423\n",
      "   1.15824342  1.39527963  1.57939033 -0.05369714 -0.05369714 -0.05369714\n",
      "  -0.05369714 -0.05369714 -0.05369714 -0.05369714 -0.05369714 -0.05369714\n",
      "  -0.05369714 -0.05369714 -0.05369714 -0.05369714]]\n"
     ]
    }
   ],
   "source": [
    "# Initialize a SEED value to ensure that the random processes in the code can be reproduced.\n",
    "SEED = 123\n",
    "\n",
    "# Call the function with seed value\n",
    "set_global_determinism(seed=SEED)\n",
    "\n",
    "# The keys for the beat data (beat_key), the target (out_key), and the features (feat_keys) are defined\n",
    "beat_key = 'bioz_beats'\n",
    "out_key = 'sys'\n",
    "feat_keys = ['phys_feat_1','phys_feat_2','phys_feat_3']\n",
    "\n",
    "# Data scaling of BP, input beats, and input features\n",
    "# This scaler standardizes by removing the mean and scaling to unit variance\n",
    "# This is done to ensure having the same scale, which can improve the performance of machine learning algorithms\n",
    "# preprocessing.StandardScaler 是Scikit-learn库中用于数据标准化的类。Standardization。\n",
    "# to_numpy() 方法将Pandas Series转换为NumPy数组，[:, None] 确保数据是二维数组的形式，即使只有一列数据。\n",
    "scaler_out = preprocessing.StandardScaler().fit(df_demo_data[out_key].to_numpy()[:, None]) \n",
    "# 标准化心跳数据\n",
    "scaler_beats = preprocessing.StandardScaler().fit(np.concatenate(df_demo_data[beat_key].to_numpy())[:, None])\n",
    "# 标准化特征数据\n",
    "scaler_X = [preprocessing.StandardScaler().fit(df_demo_data[a].to_numpy()[:, None]) for a in feat_keys]\n",
    "\n",
    "# Apply Scaling\n",
    "# The scaled versions of the BP, input beats, and input features are then added to the dataframe\n",
    "df_demo_data.loc[df_demo_data.index,beat_key+'_scaled'] = df_demo_data.apply(lambda x: np.concatenate(scaler_beats.transform(x[beat_key][:, None])), axis=1).to_numpy()   \n",
    "df_demo_data.loc[df_demo_data.index,out_key+'_scaled'] = df_demo_data.apply(lambda x: np.concatenate(scaler_out.transform(np.array([x[out_key]])[:, None]))[0], axis=1).to_numpy()\n",
    "for tmp_key, tmp_count in zip(feat_keys, range(len(feat_keys))):\n",
    "    df_demo_data.loc[df_demo_data.index, tmp_key+'_scaled'] = df_demo_data.apply(lambda x: np.concatenate(scaler_X[tmp_count].transform(np.array([x[tmp_key]])[:, None])), axis=1).to_numpy()\n",
    "\n",
    "# Fetch scaled feature names\n",
    "X_keys = [a+'_scaled' for a in feat_keys]\n",
    "\n",
    "# 查看标准化后的心跳数据的前5行\n",
    "print(\"标准化后的心跳数据前5行:\")\n",
    "print(df_demo_data[beat_key + '_scaled'].head())\n",
    "\n",
    "# 查看标准化后的目标变量的前5行\n",
    "print(\"\\n标准化后的目标变量前5行:\")\n",
    "print(df_demo_data[out_key + '_scaled'].head())\n",
    "\n",
    "# 查看所有标准化后的生理特征的前5行\n",
    "print(\"\\n标准化后的生理特征前5行:\")\n",
    "for key in X_keys:  # X_keys 包含了所有标准化特征的键名\n",
    "    print(f\"{key}: \")\n",
    "    print(df_demo_data[key].head())\n",
    "    print()\n",
    "\n",
    "# Prepare train/test using minimal training the BP\n",
    "# Fetch data shapes\n",
    "# 获取序列数据的长度\n",
    "length_seq_x = df_demo_data.apply(lambda x: len(x[beat_key+'_scaled']), axis=1).unique()[0]\n",
    "# Set the length of the target to 1\n",
    "# 设置了目标变量（血压 BP）的长度为1。这通常意味着每个心跳序列对应的是一个单独的血压值，或者模型的输出是一个标量值。\n",
    "length_seq_y = 1\n",
    "\n",
    "# Start with all points\n",
    "# Reshape the scaled beat data into a 2D array where each row corresponds to a sample and each column corresponds to a time point in the beat sequence\n",
    "# The same is done for the features and the target\n",
    "# 这行代码首先使用 np.concatenate 将DataFrame中所有行的标准化心跳数据（beat_key+'_scaled'）合并成一个一维数组。然后，使用 np.reshape 将这个一维数组重塑为一个二维数组\n",
    "all_beats = np.reshape(np.concatenate(df_demo_data[beat_key+'_scaled'].values), (len(df_demo_data), length_seq_x))\n",
    "# 将所有生理特征的标准化数据合并成一个二维数组\n",
    "[all_feat1, all_feat2, all_feat3] = [df_demo_data[a].values[:, None] for a in X_keys]\n",
    "# 将所有目标变量的标准化数据合并成一个二维数组\n",
    "all_out = df_demo_data[out_key+'_scaled'].values[:, None]\n",
    "\n",
    "# Used only for plotting purposes\n",
    "# 计算标准化后的目标变量的最大值和最小值\n",
    "#out_max_rescaled = np.concatenate(scaler_out.inverse_transform(all_out[:, 0][:, None])).max()\n",
    "#out_min_rescaled = np.concatenate(scaler_out.inverse_transform(all_out[:, 0][:, None])).min()\n",
    "\n",
    "# 逆转换 all_out 以获取原始尺度的值\n",
    "original_out = scaler_out.inverse_transform(all_out)\n",
    "\n",
    "# 计算逆转换后数组的最大值和最小值\n",
    "out_max_rescaled = original_out.max()\n",
    "out_min_rescaled = original_out.min()\n",
    "\n",
    "# Given different trials have time gaps, ignore first 3 instances from indices to prevent discontiunity in training\n",
    "# 为了避免训练数据中的不连续性，忽略了每个试验的前三个数据点\n",
    "list_all_length = [0]\n",
    "for _, df_tmp in df_demo_data.groupby(['trial_id']):\n",
    "    list_all_length.append(len(df_tmp))\n",
    "ix_ignore_all = np.concatenate(np.array([np.arange(a, a+3,1) for a in list(np.cumsum(list_all_length)[:-1])]))\n",
    "\n",
    "# Update the final indices set\n",
    "# 将所有数据点的索引分为训练集和测试集\n",
    "ix_all=list(set(np.arange(len(df_demo_data)))-set(ix_ignore_all))\n",
    "\n",
    "# Separate train/test based on minimal training criterion\n",
    "# 使用随机种子将数据集分为训练集和测试集，这行代码设置了随机数生成器的种子，以确保数据集的划分是可重复的\n",
    "random.seed(0)\n",
    "bp_dist = df_demo_data[out_key].values\n",
    "\n",
    "# Find indices for train and test datasets\n",
    "# The target values are sorted in ascending order, and the sorted indices are split into multiple subsets\n",
    "# For each subset, a random index is selected as a training index\n",
    "# np.argsort(bp_dist) 根据目标变量 bp_dist（即 df_demo_data[out_key].values）的值进行排序，并返回排序后的索引数组。\n",
    "# np.split 将排序后的索引数组分割为多个子数组，每个子数组的长度由 np.histogram 函数计算得到。\n",
    "ix_split = np.split([a for a in np.argsort(bp_dist) if a not in set(ix_ignore_all)], np.cumsum(np.histogram(bp_dist[ix_all],bins=np.arange(bp_dist[ix_all].min(), bp_dist[ix_all].max(), 1))[0]))\n",
    "# 从每个子数组中随机选择一个索引作为训练集的索引，如果子数组为空，则选择-1作为索引，表示没有数据点被选择\n",
    "ix_train = [random.Random(4).choice(a) if len(a)>0 else -1 for a in ix_split]\n",
    "ix_train = list(set(ix_train)-set([-1]))\n",
    "\n",
    "# Test set is all remaining points not used for training\n",
    "# 将所有数据点的索引分为训练集和测试集\n",
    "ix_test = list(set(ix_all) - set(ix_train))\n",
    "\n",
    "# Build train and test datasets based on the indices\n",
    "# 使用索引将数据集分为训练集和测试集\n",
    "train_beats = all_beats[ix_train, :]\n",
    "test_beats = all_beats[ix_test, :]\n",
    "[train_feat1, train_feat2, train_feat3] = [all_feat1[ix_train, :], all_feat2[ix_train, :], all_feat3[ix_train, :]]\n",
    "[test_feat1, test_feat2, test_feat3] = [all_feat1[ix_test, :], all_feat2[ix_test, :], all_feat3[ix_test, :]]\n",
    "train_out = all_out[ix_train, :]\n",
    "test_out = all_out[ix_test, :]\n",
    "\n",
    "# 查看训练集和测试集的形状\n",
    "print(f\"训练集的形状: {train_beats.shape}\")\n",
    "print(f\"测试集的形状: {test_beats.shape}\")\n",
    "\n",
    "# 输出训练集中的心跳数据的前3行\n",
    "#print(\"训练集中的心跳数据前3行:\")\n",
    "print(train_beats[:3])\n",
    "\n",
    "# 输出测试集中的心跳数据的前5行\n",
    "#print(\"\\n测试集中的心跳数据前5行:\")\n",
    "#print(test_beats[:5])\n",
    "\n",
    "# 输出训练集中的特征数据的前5行\n",
    "#print(\"\\n训练集中的特征数据前5行:\")\n",
    "#print(train_feat1[:5])\n",
    "\n",
    "# 输出训练集中的目标变量的前5行\n",
    "#print(\"\\n训练集中的目标变量前5行:\")\n",
    "#print(train_out[:5])\n",
    "\n",
    "# 输出测试集中的特征数据的前5行\n",
    "#print(\"\\n测试集中的特征数据前5行:\")\n",
    "#print(test_feat1[:5])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ab85496f-ad0e-4413-8ad5-9c84f38ba47e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Define model input tensors\n",
    "# The training, testing, and all data are converted to TensorFlow tensors\n",
    "# The tensors for the different datasets are grouped into lists \n",
    "\n",
    "# 将训练集、测试集和所有数据转换为 TensorFlow 张量\n",
    "model_inp = tf.convert_to_tensor(train_beats, dtype=tf.float32)\n",
    "feat1_inp = tf.convert_to_tensor(train_feat1, dtype=tf.float32)\n",
    "feat2_inp = tf.convert_to_tensor(train_feat2, dtype=tf.float32)\n",
    "feat3_inp = tf.convert_to_tensor(train_feat3, dtype=tf.float32)\n",
    "inp_comb = [model_inp, feat1_inp, feat2_inp, feat3_inp]\n",
    "\n",
    "# 将测试集转换为 TensorFlow 张量\n",
    "model_inp_test = tf.convert_to_tensor(test_beats, dtype=tf.float32)\n",
    "feat1_inp_test = tf.convert_to_tensor(test_feat1, dtype=tf.float32)\n",
    "feat2_inp_test = tf.convert_to_tensor(test_feat2, dtype=tf.float32)\n",
    "feat3_inp_test = tf.convert_to_tensor(test_feat3, dtype=tf.float32)\n",
    "inp_comb_test = [model_inp_test, feat1_inp_test, feat2_inp_test, feat3_inp_test]\n",
    "\n",
    "# 将所有数据转换为 TensorFlow 张量\n",
    "model_inp_all = tf.convert_to_tensor(all_beats, dtype=tf.float32)\n",
    "feat1_inp_all = tf.convert_to_tensor(all_feat1, dtype=tf.float32)\n",
    "feat2_inp_all = tf.convert_to_tensor(all_feat2, dtype=tf.float32)\n",
    "feat3_inp_all = tf.convert_to_tensor(all_feat3, dtype=tf.float32)\n",
    "inp_comb_all = [model_inp_all, feat1_inp_all, feat2_inp_all, feat3_inp_all]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e868349-c5ed-4c6c-9b2a-666e84cd1439",
   "metadata": {},
   "source": [
    "### Train the Conventional model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7543d519-1fd8-47c3-bee8-2fd9d2afe632",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conventional model training Completed. Epoch 342/5000 -- loss: 0.0099\n"
     ]
    }
   ],
   "source": [
    "#############################\n",
    "###### Conventional model\n",
    "#############################\n",
    "\n",
    "# A Deep Neural Network model is initialized with the dimension of the beats, the diemnsion of each feature, and the number of neurons in the first dense layer\n",
    "# 使用 model_DNN 函数初始化了一个深度神经网络模型，其中包含了心跳数据的维度、每个特征的维度以及第一个全连接层中的神经元数量\n",
    "model_dnn_conv = model_DNN(np.shape(train_beats)[-1], 1, 64)\n",
    "# 配置优化器，Adam优化器实例，并设置学习率为 3e-4\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=3e-4)\n",
    "\n",
    "# Two lists are initialized to keep track of the training and testing loss during each epoch\n",
    "# 初始化两个列表，用于记录每个时期的训练和测试损失\n",
    "loss_list_conv = []\n",
    "test_loss_list_conv = []\n",
    "\n",
    "epochs = 5000\n",
    "for epoch in range(epochs):\n",
    "    #创建了一个GradientTape对象，用于记录在前向传播过程中的操作\n",
    "    with tf.GradientTape() as tape:\n",
    "        \n",
    "        # 监视输入\n",
    "        tape.watch(inp_comb)      \n",
    "        # Traditional out\n",
    "        # 正向传播\n",
    "        yh = model_dnn_conv(inp_comb, training=True)\n",
    "        loss_ini = yh - train_out # 计算损失\n",
    "        loss = K.mean(K.square(loss_ini)) # 计算均方误差/损失\n",
    "        \n",
    "    grads = tape.gradient(loss, model_dnn_conv.trainable_weights) # 计算梯度\n",
    "\n",
    "    loss_list_conv.append(float(loss)) # 记录损失值\n",
    "    loss_final = np.min(loss_list_conv) # 记录最小损失值\n",
    "    optimizer.apply_gradients(zip(grads, model_dnn_conv.trainable_weights)) # 更新权重\n",
    "\n",
    "    pred_out = model_dnn_conv(inp_comb_test) #在测试集上进行预测\n",
    "    \n",
    "    test_loss_ini = pred_out - test_out # 计算测试集上的损失\n",
    "    test_loss = K.mean(K.square(test_loss_ini)) # 计算测试集上的损失\n",
    "    test_loss_list_conv.append(float(test_loss)) # 记录测试集上的损失\n",
    "    \n",
    "    # If the training loss reaches a minimum value of 0.01, or the maximum number of epochs is reached, the training process is stopped\n",
    "    # 如果训练损失达到0.01的最小值，或者达到最大时期数，训练过程将停止\n",
    "    if (loss_final<=0.01) | (epoch==epochs-1):\n",
    "        print(\"Conventional model training Completed. Epoch %d/%d -- loss: %.4f\" % (epoch, epochs, float(loss)))\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72c83887-d459-4504-9904-146bac14cfb3",
   "metadata": {},
   "source": [
    "### Train the PINN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f99ed78b-d46e-4fa8-b1a0-8799d4f5eed9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PINN model training Completed. Epoch 2124/5000 -- loss: 0.0099\n"
     ]
    }
   ],
   "source": [
    "#############################\n",
    "############### PINN MODEL\n",
    "#############################\n",
    "\n",
    "# A Deep Neural Network model is initialized with the dimension of the beats, the diemnsion of each feature, and the number of neurons in the first dense layer\n",
    "model_dnn_pinn = model_DNN(np.shape(train_beats)[-1], 1, 64) # 使用 model_DNN 函数初始化了一个深度神经网络模型\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate=10e-4) # 配置优化器，Adam优化器实例，并设置学习率为 10e-4\n",
    "\n",
    "# Two lists are initialized to keep track of the training and testing loss during each epoch\n",
    "loss_list_pinn = [] # 初始化两个列表，用于记录每个时期的训练和测试损失\n",
    "test_loss_list_pinn = [] \n",
    "\n",
    "epochs = 5000 #设置了训练迭代的次数为5000\n",
    "for epoch in range(epochs):\n",
    "    with tf.GradientTape() as tape: #创建了一个GradientTape对象，用于记录在前向传播过程中的操作\n",
    "\n",
    "        tape.watch(inp_comb)      # 监视输入\n",
    "        # Traditional out\n",
    "        # 计算传统损失\n",
    "        yh = model_dnn_pinn(inp_comb, training=True) # 正向传播\n",
    "        loss_ini = yh - train_out # 计算预测输出和实际输出间的差异\n",
    "        loss = K.mean(K.square(loss_ini))        # 计算均方误差/损失\n",
    "        \n",
    "        # Additional tf.GradientTape contexts are used to compute the derivatives of the model's predictions with respect to the features \n",
    "        # 额外的 tf.GradientTape 上下文用于计算模型预测相对于特征的导数\n",
    "        with tf.GradientTape() as deriv_f1: #创建了一个GradientTape对象，用于记录在前向传播过程中的操作\n",
    "            deriv_f1.watch(inp_comb_all) # 监视输入\n",
    "            yhp = model_dnn_pinn(inp_comb_all, training=True) #执行模型前向传播\n",
    "        dx_f1 = deriv_f1.gradient(yhp, feat1_inp_all) # 计算模型预测相对于特征1的导数\n",
    "\n",
    "        with tf.GradientTape() as deriv_f2:\n",
    "            deriv_f2.watch(inp_comb_all)\n",
    "            yhp = model_dnn_pinn(inp_comb_all, training=True)\n",
    "        dx_f2 = deriv_f2.gradient(yhp, feat2_inp_all) # 计算模型预测相对于特征2的导数\n",
    "\n",
    "        with tf.GradientTape() as deriv_f3:\n",
    "            deriv_f3.watch(inp_comb_all)\n",
    "            yhp = model_dnn_pinn(inp_comb_all, training=True)\n",
    "        dx_f3 = deriv_f3.gradient(yhp, feat3_inp_all) # 计算模型预测相对于特征3的导数\n",
    "\n",
    "        # A physics-based prediction is computed by adding the model's predictions to the product of the computed derivatives and\n",
    "        # the differences in the feature values between consecutive timesteps\n",
    "        # 通过将模型的预测值与计算得到的导数乘以连续时间步之间特征值的差异相加，计算出基于物理的预测\n",
    "        pred_physics = (yhp[:-1, 0]\n",
    "                        +Multiply()([dx_f1[:-1, 0], feat1_inp_all[1:, 0] - feat1_inp_all[:-1, 0]])\n",
    "                        +Multiply()([dx_f2[:-1, 0], feat2_inp_all[1:, 0] - feat2_inp_all[:-1, 0]])\n",
    "                        +Multiply()([dx_f3[:-1, 0], feat3_inp_all[1:, 0] - feat3_inp_all[:-1, 0]])\n",
    "                        )\n",
    "\n",
    "        physics_loss_ini = pred_physics - yhp[1:, 0] # 计算基于物理的损失\n",
    "        physics_loss = K.mean(K.square(tf.gather_nd(physics_loss_ini,indices = np.array(ix_all[:-1])[:, None])))  # 计算均方误差/损失\n",
    "    \n",
    "        # The total loss is computed as the sum of the initial loss and ten times the physics-based loss\n",
    "        # The physics-based loss is multiplied by a factor of ten to emphasize its importance in the loss function\n",
    "        loss_total = loss + physics_loss * 10 # 计算总损失，将物理损失乘以10以强调其在损失函数中的重要性\n",
    "\n",
    "    grads = tape.gradient(loss_total, model_dnn_pinn.trainable_weights) # 计算梯度\n",
    "\n",
    "    loss_list_pinn.append(float(loss)) # 记录损失值\n",
    "    loss_final=np.min(loss_list_pinn)  # 记录最小损失值\n",
    "    optimizer.apply_gradients(zip(grads, model_dnn_pinn.trainable_weights)) # 更新权重\n",
    "    \n",
    "    pred_out = model_dnn_pinn(inp_comb_test) #在测试集上进行预测\n",
    "    test_loss_ini = pred_out - test_out      # 计算测试集上的损失\n",
    "    test_loss = K.mean(K.square(test_loss_ini)) # 计算测试集上的损失\n",
    "    test_loss_list_pinn.append(float(test_loss)) # 记录测试集上的损失\n",
    "    \n",
    "    # If the training loss reaches a minimum value of 0.01, or the maximum number of epochs is reached, the training process is stopped\n",
    "    # 如果训练损失达到0.01的最小值，或者达到最大时期数，训练过程将停止\n",
    "    if (loss_final<=0.01) | (epoch==epochs-1): \n",
    "        print(\"PINN model training Completed. Epoch %d/%d -- loss: %.4f\" % (epoch,epochs,float(loss)))\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7776218f-592d-4278-9ed6-da9326f2fd66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#### Conventional Performance ####\n",
      "Corr: 0.86,  RMSE: 10.2\n",
      "----------------------------------\n",
      "#### PINN Performance ####\n",
      "Corr: 0.95,  RMSE: 5.3\n"
     ]
    }
   ],
   "source": [
    "#The trained model's predictions on the test dataset are computed\n",
    "# 在测试集上计算训练模型的预测\n",
    "pred_out = model_dnn_conv(inp_comb_test) #传统模型，测试集上的预测\n",
    "\n",
    "#The Pearson correlation coefficient and the Root Mean Square Error are calculated between the actual and predicted test outcomes\n",
    "# 计算实际和预测测试结果之间的皮尔逊相关系数和均方根误差\n",
    "corr_conv = np.corrcoef(np.concatenate(test_out)[:], np.concatenate(pred_out)[:])[0][1] # 计算皮尔逊相关系数\n",
    "rmse_conv = np.sqrt(np.mean(np.square # 计算均方根误差\n",
    "                           (np.concatenate(scaler_out.inverse_transform(np.concatenate(test_out)[:][:, None]))-\n",
    "                            np.concatenate(scaler_out.inverse_transform(np.concatenate(pred_out)[:][:, None]))))) # 逆转换并计算均方根误差\n",
    "\n",
    "pred_out = model_dnn_pinn(inp_comb_test) #PINN模型，测试集上的预测\n",
    "corr_pinn = np.corrcoef(np.concatenate(test_out)[:], np.concatenate(pred_out)[:])[0][1] # 计算皮尔逊相关系数\n",
    "rmse_pinn = np.sqrt(np.mean(np.square(\n",
    "    np.concatenate(scaler_out.inverse_transform(np.concatenate(test_out)[:][:, None]))-\n",
    "    np.concatenate(scaler_out.inverse_transform(np.concatenate(pred_out)[:][:, None]))))) # 逆转换并计算均方根误差，即均方根误差\n",
    "\n",
    "print('#### Conventional Performance ####')\n",
    "print('Corr: %.2f,  RMSE: %.1f'%(corr_conv, rmse_conv)) # 输出传统模型的性能\n",
    "print('----------------------------------')\n",
    "print('#### PINN Performance ####')\n",
    "print('Corr: %.2f,  RMSE: %.1f'%(corr_pinn, rmse_pinn)) # 输出PINN模型的性能"
   ]
  },
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   "execution_count": 38,
   "id": "bf77d2b1-8f28-4d2c-a5e9-367dac4dc509",
   "metadata": {},
   "outputs": [
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 const render_items = [{\"docid\":\"20c187d7-7186-4e19-9e97-0b67aa024e95\",\"roots\":{\"p2317\":\"d13650f8-41c9-40a0-aca8-74a82d5146f9\"},\"root_ids\":[\"p2317\"]}];\n  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    let attempts = 0;\n    const timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p2317"
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制测试数据集的预测血压值和实际血压值\n",
    "# 创建图形\n",
    "s=figure(width=770,height=400,y_range=(out_min_rescaled-20,out_max_rescaled+20))\n",
    "# 绘制传统模型的预测值 \n",
    "s.scatter(ix_test,np.concatenate(scaler_out.inverse_transform(np.concatenate(model_dnn_conv(inp_comb_test))[:,None])),size=7,line_color=None,color=palettes.Colorblind8[5],legend_label='Conv.')\n",
    "# 绘制PINN模型的预测值\n",
    "s.scatter(ix_test,np.concatenate(scaler_out.inverse_transform(np.concatenate(model_dnn_pinn(inp_comb_test))[:,None])),size=7,line_color=None,color=palettes.Colorblind8[3],legend_label='PINN')\n",
    "# 绘制真实血压值\n",
    "s.line(list(range(len(df_demo_data))),np.concatenate(scaler_out.inverse_transform(all_out[:,0][:,None])),line_width=3,line_color='black',line_alpha=1,line_dash='dashed',legend_label='True BP')\n",
    "s.xaxis.axis_label='Beat time (s)'\n",
    "s.yaxis.axis_label='SBP (mmHg)'\n",
    "\n",
    "\n",
    "\n",
    "# 绘制损失值\n",
    "s2 = figure(width=500,height=400,y_axis_type=\"log\",y_range=(1e-2,2))\n",
    "# 传统模型的损失值\n",
    "s2.line(np.linspace(0,100,len(loss_list_conv)),loss_list_conv,line_width=3,color='black',legend_label='PINN-test')\n",
    "# 传统模型的测试集损失值\n",
    "s2.line(np.linspace(0,100,len(test_loss_list_conv)),test_loss_list_conv,line_width=3,line_dash='dashed',color='black',legend_label='Conv-test')\n",
    "# PINN模型的损失值\n",
    "s2.line(np.linspace(0,100,len(loss_list_pinn)),loss_list_pinn,line_width=3,alpha=0.8,color='orange',legend_label='PINN-train')\n",
    "# PINN模型的测试集损失值\n",
    "s2.line(np.linspace(0,100,len(test_loss_list_pinn)),test_loss_list_pinn,line_width=3,line_dash='dashed',color='orange',legend_label='Conv-train')\n",
    "\n",
    "s2.xaxis.axis_label = 'Training percent (%)'\n",
    "s2.yaxis.axis_label = 'mse norm.'\n",
    "\n",
    "figure_settings(s)\n",
    "figure_settings(s2)\n",
    "\n",
    "show(row (s,s2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e1776ae4-7d3a-4ccf-837d-4ccac58e89fa",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'Row' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plot \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43ms2\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m (plot)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m (\u001b[38;5;28mtype\u001b[39m(plot))\n",
      "\u001b[0;31mTypeError\u001b[0m: 'Row' object is not iterable"
     ]
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3587427c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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